DeepUnitMatch: tracking neurons across days in electrophysiology using Deep Neural Networks

Abstract

To understand neural processes such as learning or memory, we need to track the activity of populations of neurons at the level of single spikes and across days. Here, we leverage deep neural networks to build DeepUnitMatch, a software that reliably tracks individual neurons in high-density electrophysiological recordings across weeks. DeepUnitMatch uses only the spike waveforms of the neurons, and not their spiking patterns, and outperforms current solutions.